Computer assisted planning of liver treatments is primarily implemented using computed tomography (CT). The computer assisted planning can be an important aid for surgery decisions and visualization of individual patient anatomy in three dimensions (3D). The liver treatments include minimally invasive therapies, oncology liver sectioning, and living donor transplantation.
The computer assisted planning of liver treatments is based on the liver volume, the anatomical liver segments, the vessel structure, and the relation of lesions to these structures. Detection of boundaries between liver segments is the first step of the preoperative planning. Radiologists currently use CT images with intravenous contrast infusion, in order to detect lesions and vessels in the liver. The key point of the above-mentioned treatments is the liver volume segmentation.
There are several published methods about segmentation of CT images. Most of these CT image segmentation methods are some variants of the region growing, active contour/surface, level-set, thresholding and/or classification algorithms. In addition, the CT image segmentation methods are often based on a statistical, anatomical, or geometric model.
Unfortunately, only a few CT image segmentation methods having been tested clinically. In most cases, CT segmentation methods are merely simulated with none or very little patient data. Usually—with a few exceptions—“general organ segmentation methods” are disclosed that are intended to segment “every organ” in “every modality.” However, very little statistical evaluation information is available on the general organ segmentation methods is available. Thus, the reliability and effectiveness of the general organ image segmentation methods are highly doubtful.
Some conventional methods to automatically segment the liver image from contrast-enhanced CT image scans delineate the skin, bones, lungs, kidneys and spleen, by combining the use of thresholding, mathematical morphology and distance maps before extracting an image of the liver. In these conventional methods, a 3D reference model is generated from manually segmented image of a liver and adjusted onto the image with rigid and affine registration. The 3D reference model is deformed to get the final result. The weakness of these conventional methods is that only one phase of the contrast-enhanced examination is used, which is acquired according to a special protocol and does not correspond to the general practice.
Other conventional methods of segmenting an image of a liver from a CT image that also use a 3D statistical shape model include an iterative technique that first builds a statistical model from a training set of shapes. Each shape is given by M points sampled on its surface. The points must correspond in an anatomically meaningful way, and the coordinates must be given relative to a common reference frame. The next step is positioning a mean shape into the image data. At each iteration, several position adjustments are performed until no further significant improvement is achieved. Then single shape adjustment is applied. Unfortunately, no clinical evaluation of this method has been performed.
The level-set methods are other conventional methods of organ segmentation. One advantage of the level-set methods is that topological changes are managed and the problem is defined in one higher dimension. The main disadvantage is that all level-set methods are very time-consuming and produce leakage.
An active-contour method is used in clinical to segment abdominal organs. The active-contour method works well on native images, because the organs are homogenous. In case of contrast-enhanced images, the contrast agent is cumulated differently in different parts of the liver. For example the vessels and some tumors will have greater intensity values than the liver parenchyma. The active contour method starts from a smaller region and expands the smaller regions to fit the surface to the contour of the organ. The vessels and tumors set back the regular growing of the surface.
A region-growing based approach to segment organ images can be used with good results on contrast-enhanced images. The method starts from a small region (environment of input curve, or point), and every neighboring voxel is added to the actual region, if intensity of the voxel corresponds to a pre-defined range. Region-growing methods can close round the vessels and tumors, but region-growing methods are very sensitive for the input and can easily flow out to other organs that have similar intensities.